Interpretive Summary: The Root Zone Water Quality Model (RZWQM2) is a one dimensional model simulating agricultural management effects on crop production and soil and water quality. This paper outlines the principles of calibrating the model component by component. Users should consult the RZWQM2 user manual distributed along with the model and a more detailed protocol on how to calibrate RZWQM2 in a book chapter. A case study illustrates the use of field versus laboratory measured soil hydraulic properties on simulated soil water, soil N, and crop production. It also demonstrates the interaction between soil and plant parameters in simulated plant responses to water stresses. The commonly used trial and error calibration method worked well for experienced users. However, the Parameter Estimation Software (PEST) optimization was more efficient than a grid search of model parameters. In addition, PEST provides sensitivity and uncertainty analyses that should help users in selecting the right parameters to calibrate.

Technical Abstract:
The Root Zone Water Quality Model (RZWQM2) has been used widely for simulating agricultural management effects on crop production and soil and water quality. Although it is a one-dimensional model it has many desirable features for the modeling community. This paper outlines the principles of calibrating the model component by component. Users should consult the RZWQM2 user manual distributed along with the model and a more detailed protocol on how to calibrate RZWQM2 in a book chapter. A case study illustrates the use of field versus laboratory measured soil hydraulic properties on simulated soil water, soil N, and crop production. It also demonstrates the interaction between soil and plant parameters in simulated plant responses to water stresses. The commonly used trial and error calibration method worked well for experienced users. However, an automated calibration procedure was more objective. The Parameter Estimation Software (PEST) optimization was more efficient than a grid search of model parameters. In addition, PEST provides sensitivity and uncertainty analyses that should help users in selecting the right parameters to calibrate.